Enterprises store and process their large amounts of data in a variety of ways. One manner in which enterprises store data is by using relational databases and corresponding relational database management systems (RDBMS). Such data, usually referred to as structured data, may be collected, normalized, formatted and stored in an RDBMS. Tools based on standardized data languages such as the Structured Query Language (SQL) may be used for accessing and processing structured data. However, it is estimated that such formatted structured data represents only a tiny fraction of an enterprise's stored data. Organizations are becoming increasingly aware that substantial information and knowledge resides in unstructured data (i.e., “Big Data”) repositories. Accordingly, simple and effective access to both structured and unstructured data are seen as necessary for maximizing the value of enterprise informational resources.
However, conventional platforms that are currently being used to handle structured and unstructured data can substantially differ in their architecture. In-memory processing and Storage Area Network (SAN)-like architectures are used for traditional SQL queries, while commodity or shared nothing architectures (each computing node, consisting of a processor, local memory, and disk resources, shares nothing with other nodes in the computing cluster) are usually used for processing unstructured data. An architecture that supports both structured and unstructured queries can better handle current and emerging Big Data applications.